Abstract
The uncertainty of rainfall estimated from averages of discrete samples collected by a satellite is assessed using a multiyear radar dataset covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100-, 200-, and 500-km space domains; 1-, 5-, and 30-day rainfall accumulations; and regular sampling time intervals of 1, 3, 6, 8, and 12 h. These extensive analyses are combined to characterize the sampling uncertainty as a function of space and time domain, sampling frequency, and rainfall characteristics by means of a simple scaling law. Moreover, it is shown that both parametric and nonparametric statistical techniques of estimating the sampling uncertainty produce comparable results. Sampling uncertainty estimates, however, do depend on the choice of technique for obtaining them. They can also vary considerably from case to case, reflecting the great variability of natural rainfall, and should therefore be expressed in probabilistic terms. Rainfall calibration errors are shown to affect comparison of results obtained by studies based on data from different climate regions and/or observation platforms.
Corresponding author address: Dr. Matthias Steiner, Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544. Email: msteiner@princeton.edu
